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Relationship Between Drug Targets and Drug-Signature Networks: A Network-Based Genome-Wide LandscapeChaewon Lee 

CHA UniversitySungmin Kim 

CHA UniversitySoonok Sa 

CHA UniversitySang-Min Nam 

CHA UniversityHyun Wook Han  ( stepano7@gmail.com )

CHA University

Research Article

Keywords: cellular mechanism, FDA, drug-sensitive score, KDTN and KDSN, drug signatures

Posted Date: November 18th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-967821/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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AbstractDrugs produce pharmaceutical and adverse effects that arise from the complex relationship betweendrug targets and signatures; by considering such relationships, we can begin to understand the cellularmechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding totargets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs)and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which geneexpression is stimulated by the drug). Accordingly, we constructed two drug–gene bipartite networkmodels, a drug-target network and drug-signature network, which were merged into a multidimensionalmodel. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationshipsin terms of their biological network structure and gene function. A symmetric balance between the KDTNand KDSN of genes facilitates the possibility of therapeutic drug effects in living organisms. Theseresults provide new insights into the relationship between drugs and genes, speci�cally drug targets anddrug signatures.

IntroductionDrugs produce pharmaceutical and adverse effects according to the complex relationship between drugtargets and drug signatures (1). As gene analysis has become more prevalent, studies on the associationbetween genes and drugs have also become more widespread in the �eld of drug research. As examples,Nagaraj et al. used a computational drug-repositioning approach to rapidly identify potent drugcandidates for epithelial ovarian cancer treatment (2), Kim et al. assessed reversal gene expressionpro�les for gastric cancer using computational drug repositioning (3), and Grenie and Hu investigateddrugs for in�ammatory bowel disease using genetic information and computational methods (4). In suchstudies, genes are divided into two categories: drug-target genes and drug-signature genes. Drug-targetgenes (also known as “druggable genes”) code for proteins that physically bind with the drug compound(5, 6), whereas drug-signature genes (also known as “drug-sensitive” genes) are differentially expresseddue to drug–protein binding following drug treatment (1). Studying the interactions between drug targetsand drug signatures is important for drug discovery, drug repositioning, and identifying inference frompotential adverse drug reactions (7, 8).

Ideally, the study of drug-related genes in both categories should extend to all human genes without anylimitations. However, conventional studies have been limited to speci�c genes and speci�c drugs relatedto only a few diseases, and they have focused on only drug targets or drug signatures. Because suchstudies have investigated speci�c genes and drugs, the characteristics of all human genes in living cellsare not typically taken into account. Additionally, for these reasons, conventional drug developmentstudies may show effects on the phenotype of interest, and the presence of treated subjects is typicallyperceived as a cause of bias in genome-wide association studies (9). Therefore, in the present study, wefocused on the relationship between drug targets and signatures based on their characteristics. Our aim

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was to identify the genetic landscape via a multidimensional network using genome-wide drug–genebinding data and gene expression data.

In general, drugs affect the activity of proteins that correspond to target genes. During drug treatment,biological networks are disturbed and the expression of many other genes is signi�cantly changed by

unexpected responses to the drug. Thus, the pharmaceutical and adverse effects of the drug occurthrough complex relationships among drug targets and drug signatures (1). Changes in gene expressionby drug treatment can imply a therapeutic effect at the cellular level. From another perspective, drugtreatment can have a perturbative effect in cells via gene networks (10). Indeed, changes in geneexpression in cells to maintain homeostasis arise due to perturbation (11).

To elucidate genome-wide inter-relationships between drug-target genes and signatures, we selectedgenes corresponding to targets and signatures for drugs that have both protein binding information fordrug-target score (KDTN) and microarray signature information for drug-sensitive score (KDSN). KDTNrepresents the degree to which the protein corresponding to the gene binds to a large number of drugs,whereas KDSN represents the degree of the gene expression response following stimulation by drugs.Overall, we explored the network-based genome-wide landscape by comparing the cellular and functionalcharacteristics of drug targets and drug signatures using the two variables KDTN and KDSN.

Material And MethodsData

The drug and genome database DSigDB (http://dsigdb.tanlab.org/DSigDBv1.0/) (12) is an open-sourcedatabase that currently includes 22,527 gene sets and consists of 17,389 unique compounds covering19,531 genes. Gene sets provide seamless integration by which to link gene expression withdrugs/compounds. DSigDB organizes drugs and small molecule-related gene sets into four domainsbased on data for drug-induced quantitative inhibition and/or changes in gene expression. The data fromDSigDB contains four domains (D1–D4) that collect drug and genome data for four purposes, as shownin Table 1. The D1 and D3 domain datasets were used to construct each drug-target network (DTN) anddrug-sensitive network (DSN) in this study.

Construction of the network

FDA-approved drugs for which both protein binding data and microarray experiment data were availablewere used from the DSigDB database. Following these principles, the D1 and D3 datasets were used andmatched to their drug and gene ID terms.

In the process of matching terms from each domain, the Pubchem compound term(https://pubchem.ncbi.nlm.nih.gov/) (16) and Entrez term(https://www.ncbi.nlm.nih.gov/Web/Search/entrezfs.html) (17) were used as the drug and gene ID.Consequently, 382 drug compounds and 9,490 genes intersecting with D1 and D3 were extracted. Using

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these data, the DTN and DSN were constructed from calculated gene scores, i.e., KDTN and KDSN, basedon the relationship between drugs and genes as follows:

 

where the range of KDTN and KDSN was from 1 to 186 and from 1 to 104, respectively, and the number ofgenes with KDTN ≥ 1 and KDSN ≥ 1 was 720 and 9,233, respectively. Among these genes, 463simultaneously had both KDTN ≥ 1 and KDSN ≥ 1. From the relationship between the KDTN and KDSN ofgenes, the genes were divided into three groups as shown in Figure 1A: the DTG set (drug-target genes:257 genes), DSG set (drug-sensitive genes: 8,770 genes), and the DTSG set (drug-target and -sensitivegenes: 463 genes).

Figure 1B shows the process by which a drug–gene network was constructed. Two kinds of bipartitenetwork (DTN and DSN) were constructed, which were then merged into a multidimensional network. Forexample, Gene A binds to three drugs among nine drugs in the DTN (drug 2, 3, and 4) but its expressionwas changed by only one drug in the DSN (drug 7). Using such analyses, the target score and drug-sensitive score of each gene from the binary network model were calculated.

Analysis

Gene enrichment analysis and network analysis were conducted in three groups of genes (DTG, DTSG,and DSG) using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathwayanalyses via DAVID (https://david.ncifcrf.gov/) (18). The top 10 GO terms and top 8 KEGG pathway termsfor each gene group, i.e., those terms that were most enriched, were determined and are shown in Figure5. The distributions among KDTN of D1, KDSN of D3, and KDTN and KDSN of DTSG were compared usingthe ggplot package in R. We then constructed a drug-target bipartite network for the drugs and genesinvolved in the DTSG set using Cytoscape. For network visualization, 19 drugs and 170 genes were used;these 19 drugs represented about 5% of the 371 drugs included in the DTSG set. Furthermore, networkanalysis was conducted using the concept of m-core decomposition to analyze the central functionaccording to KDTN and KDSN. A “peeling algorithm” aims to characterize a network hub and elucidate the

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relationships between nodes based on network connectivity (19). In a multidimensional network, thenodes represent the drugs and genes of the DTG and DSG sets, respectively, and the edges represent therelationships between drug-target genes or drug signatures. In the present study, we applied a peelingalgorithm represented by m-core (19). Speci�cally, m-coreDSN and m-coreDTN are de�ned as themaximal connected subgraph of the DSN and DTN, respectively, in which all genes have a degree ofKDSN and KDTN greater than the m value.

ResultsGene set analysis

Distribution of KDTN and KDSN in the DTSG set. First, the distributions of KDT and KDS for the DTSG setwere analyzed. Each distribution and the three-dimensional distribution for DTSG set were identi�ed toelucidate the relationships between KDT and KDS. As shown in Figure 2, KDT and KDS had scale-free andpower-law distributions.

Construction of the drug–gene network. The distributions of KDT and KDS for the DTSG set werecompared with the D1 and D3 sets to determine whether the DTSG set had representativeness for the D1and D3 set and to show the tendency of the DTSG set before a drug–gene network was constructed.Results showed that the distributions of KDT and KDS for the DTSG set presented representativeness forD1 and D3 (Fig. 3). Once representativeness was con�rmed, an integrative drug–gene interaction networkfor the DTSG set and the drugs was visualized. However, visualizing all the target genes and sensitivegenes made it di�cult to intuitively observe the characteristics of the network. Therefore, only the targetgenes and sensitive genes for about 5% of the 371 drugs included in the DTSG set were analyzed inalphabetical order.

To construct a drug-target subnetwork, 16 drugs binding to 84 genes were extracted. To construct a drug-signature subnetwork, 13 drugs that affect the expression level of 103 genes were extracted. The drug-target subnetwork and the drug-signature subnetwork were merged as a drug–gene subnetworkcontaining 189 nodes (19 drug and 170 genes) and 266 edges. As shown in Figure 4 (in which the black-colored nodes represent drugs, the orange-colored nodes represent sensitive genes, and the green-colorednodes represent target genes), PTGER2 (purple-colored node) was the only gene included as both a targetgene and a sensitive gene for 19 drugs in the subnetwork. Except for PTGER2, the relationships betweenthe target genes and sensitive genes were exclusive and independent (Fig. 4).

Enrichment analysis

GO/KEGG pathway analysis for the DSG, DTSG, and DTG sets. Through GO analysis, the cellularcomponents, biological processes, and molecular functions associated with each gene set wereinvestigated. In the DTG set, 257 genes were associated with 13 cellular components, 28 biologicalprocesses, 20 molecular functions, and 11 KEGG pathways (FDR-adjusted p-value <0.05). In the DSG set,8,770 genes were associated with 41 cellular components, 63 biological processes, 21 molecular

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functions, and 8 KEGG pathways (FDR-adjusted p-value <0.05). In the DTSG set, 463 genes wereassociated with 24 cellular components, 95 biological processes, 35 molecular functions, and 28 KEGGpathways (FDR-adjusted p-value <0.05).

GO analysis revealed that most proteins synthesized by drug-sensitive genes were located in inner cellularzones such as the nuclear chromosome, nuclear pore, and nucleosome rather than in outer cellar zonessuch as the cell wall. The proteins synthesized by drug-sensitive genes were shown to be involved in genetranscription, gene expression regulation, and DNA replication, and to function in DNA, RNA, and proteinbinding. In contrast, GO analysis showed that most proteins synthesized by drug-target genes werelocated in outer cellular zones and played roles, for example, in receptor complexes, voltage-gatedchannel complexes, synapses, and cell junctions. Most proteins synthesized by drug-target genes werefound to be involved in the catabolic process of cGMP and cAMP and in transmission and transportprocesses; they played roles in ion channels and enzyme activity.

Through KEGG pathway analysis (Fig. 5), it was revealed that drug-sensitive genes were involved incentral dogma-related pathways such as the spliceosome, transcriptional regulation, and protein-processing progress. However, drug-target genes were involved in neural signaling pathways such asaddiction to nitrogen, nicotine, and morphine, serotonergic synapses, and retrograde endocannabinoidsignaling.

As shown in Figure 5, only 6 terms (GO and KEGG) from 114 terms associated with the three gene sets(DTG, DSG, and DTSG) overlapped. Two cellular component terms overlapped in the DTG and DTSG sets:postsynaptic membrane and voltage-gated calcium channel complex. Of the molecular function terms,3′,5′-cyclic-nucleotide phosphodiesterase activity and 3′,5′-cyclic-AMP phosphodiesterase activityoverlapped in the DTG and DTSG sets. From the biological processes terms, only one term overlappedbetween the DTG and DTSG sets: cAMP catabolic process. Similarly, one KEGG pathway term, morphineaddiction pathway, overlapped in the DTSG and DTG sets. These results suggest that drug-target genesand drug-sensitive genes are exclusive and independent in terms of their cellular locations, geneticfunctions, processes, and pathways.

Transcription factor (TF) analysis

In gene set analysis, it is important not only to characterize the gene set but also to identify the numberand type of TFs as this can help to improve understanding of gene regulatory networks. Thus, weexamined whether there were differences in the number of TFs involved in each gene set. We usedX2Kweb (20) as a TF analysis tool to examine the binding frequency and types of TFs for each gene set.Results showed that TFs bound on DNA strands on average six times per gene in the DSG set, which wasthree-fold greater than the TF binding in the DTG and DTSG sets (both two times per gene on average,Fig.6a). In total, 737 TFs were associated with the three gene sets. Of these, 30 TFs overlapped betweentwo or more gene sets as shown in Figure 6b. Therefore, the TFs involved in each gene set differed. Of the30 overlapping TFs, 9 were derived from essential genes in humans (<10% of all human genes areconsidered essential) (21).

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Core gene analysis of DTN and DSN

Characterization of the core genes in KDT and KDS for the DTG and DSG sets was examined by applyinga peeling algorithm. Each network that included >50 genes was analyzed according to m-core. Thus, m-coreDSN had 1 to 36 core gene groups whereas m-coreDTN had 1–17 core gene groups. Figure7 indicates the gene ontological characterization in each network according to m-core. In cellularcomponent analysis, the core genes of each DSN and DTN showed exclusive distributions. Proteinssynthesized by core genes of the DSN were located in the cytosol, cytoplasm, nuclear chromosome, andnucleosome. Conversely, proteins synthesized by the core genes of the DTN were located in the synapses,dendrites, plasma membrane, and axon terminus. In molecular function analysis, the core genes of eachDTN and DSN were also exclusively distributed. Proteins synthesized by the core genes of the DSNfunctioned during cell–cell adhesion and in protein heterodimerization activity by binding proteins andcadherin. The proteins synthesized by the core genes of the DTN functioned in ion binding, hormonebinding, chemical receptor activity, and enzyme activity functions (Supplementary Fig. 1A). In biologicalprocesses analysis, the core genes of the DSN and DTN networks also showed exclusive distributions.Proteins synthesized by the core genes of the DSN were involved in the PERK-mediated unfolded proteinresponse, response to hypoxia, positive regulation of angiogenesis, and regulation of cell death. Incontrast, proteins synthesized by the core genes of the DTN were involved in the response to drugs,dopamine transport, receptor signaling pathways, and monoterpenoid metabolic processes(Supplementary Fig. 1B).

In summary, characterizations of genes in the DSG, DTG, and DTSG sets in terms of GO and KEGGpathways could clearly be distinguished. In addition, the numbers and types of TFs differed among theDSG and DTG sets with different binding frequencies of the TFs on DNA strands. Finally, m-core analysisof the core genes in each DSN and DTN exhibited reciprocal balanced characteristics.

DiscussionHere, we investigated the relationships between two major gene groups, i.e., target and signature genes,according to various perspectives related to drug discovery and development. Our purpose was not onlyto investigate the relationship between DTG and DSG sets but also to demonstrate the genome-widelandscape of drug–gene multidimensional networks based on relationships between DTNs and DSNs.We classi�ed genes into three sets based on their operational roles such as their physical binding and/orresponse to drugs. First, we constructed DTNs and DSNs from experimental data such as drug-target anddrug-signature data. Subnetworks were constructed using experimental data from drug targets and drugsignatures. In a multidimensional network model constructed by merging each subnetwork, the KDTN andKDSN of the DTG, DSG, and DTSG sets were calculated; thus, the mean network interaction degree ofeach subnetwork was calculated. The three gene sets, namely DTG, DSG, and DTSG, were compared infour ways: 1) the distribution between KDTN and KDSN, 2) GO and KEGG pathway analysis, 3) thenumber and type of TFs, and 4) GO analysis with m-coreDSN and m-coreDTN according to KDSN andKDTN.

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Sorting gene processes using D1 and D3 showed that drug targets are not usually affected by the drug asreported in a previous conventional study (22). Therefore, only 463 genes intersected between 720 DTGand 9,233 DSG sets. The degree distributions of KDTN, KDSN, and subnetwork visualization showed thatthe relationship between the DTG and DSG sets was exclusive. Each degree of KDTN and KDSN showed apower-law distribution and their relationship was reciprocal. This is indirect evidence that the response ofcells to drugs is structured and organized systematically (23, 24).

The relationships of GO terms (cellular component, molecular functions, and biological process) andKEGG pathways between the DTG, DSG, and DTSG sets was also reciprocal. This shows that the DTGand DSG sets have distinct functional differences in cells. Thus, studies to investigate target genes, notsignature genes, should be conducted according to aspects of GO and KEGG pathways, as shown in thecurrent study.

TF analysis of each gene set showed that the average binding frequency of TFs involved in the DSG setwas six times per gene. The TFs involved in each gene set were also exclusive and different. Thus, genesacting as drug signatures seem to be regulated with binding frequencies three-fold greater than those ofgenes acting as drug targets. Consequently, genes regulated with 1–3 TFs would be good candidates fordrug-target genes.

GO analysis using the m-core of each network showed that the functional and spatial characteristics ofthe target gene core and the signature gene core differ. Mutually exclusive characteristics were alsoexhibited.

In conclusion, the expression of target genes was barely affected by drug treatments. Therefore, thepharmaceutical effect of drugs was due to the DSGs for which expression levels were signi�cantlychanged by drug treatment rather than the direct action of DTGs. These complex drug–gene relationshipscan produce drug side effects as well as therapeutic effects. This study provides a potential newapproach to discovering drugs. However, further studies are needed to identify the therapeutic effects andadverse drug reactions associated with the relationship between the DTN and DSN.

DeclarationsEthics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

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The datasets used and/or analysed during the current study are available from the corresponding authoron reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Basic Science Research Program through the National ResearchFoundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [grant number NRF-2020R1F1A1068423], the Ministry of Science and ICT [grant number NRF-2019M3C7A1032262] and theTechnology Innovation Program (or Industrial Strategic Technology Development Program(20015086, Development of AI-based drug adverse events monitoring service.) funded By the Ministry ofTrade, Industry & Energy(MOTIE, Korea).

Author’s contributions

H.W.H. conceived the project; H.W.H and C.W.L. designed the research; C.W.L. performed the research;C.W.L. analyzed the data; and H.W.H., C.W.L., S.M.K., S.O.S., and S.M.N. wrote the paper.

Acknowledgements

Not applicable

References1. Huang Y, Furuno M, Arakawa T, Takizawa S, de Hoon M, Suzuki H, et al. A framework for

identi�cation of on- and off-target transcriptional responses to drug treatment. Sci Rep.2019;9(1):17603.

2. Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, et al. Using a novel computationaldrug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancertreatment. Oncogene. 2018;37(3):403-14.

3. Kim IW, Jang H, Kim JH, Kim MG, Kim S, Oh JM. Computational Drug Repositioning for GastricCancer using Reversal Gene Expression Pro�les. Sci Rep. 2019;9(1):2660.

4. Grenier L, Hu P. Computational drug repurposing for in�ammatory bowel disease using geneticinformation. Comput Struct Biotechnol J. 2019;17:127-35.

5. Finan C, Gaulton A, Kruger FA, Lumbers RT, Shah T, Engmann J, et al. The druggable genome andsupport for target identi�cation and validation in drug development. Sci Transl Med. 2017;9(383).

�. Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M. Drug-target network. Nat Biotechnol.2007;25(10):1119-26.

Page 10/18

7. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol.2011;162(6):1239-49.

�. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, et al. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.Nat Commun. 2017;8(1):573.

9. Schmidt AF, Heerspink HJL, Denig P, Finan C, Groenwold RHH. When drug treatments bias geneticstudies: Mediation and interaction. PLoS One. 2019;14(8):e0221209.

10. Santolini M, Barabasi AL. Predicting perturbation patterns from the topology of biological networks.Proc Natl Acad Sci U S A. 2018;115(27):E6375-E83.

11. Moschovi M, Critselis E, Cen O, Adamaki M, Lambrou GI, Chrousos GP, et al. Drugs acting onhomeostasis: challenging cancer cell adaptation. Expert Rev Anticancer Ther. 2015;15(12):1405-17.

12. Yoo M, Shin J, Kim J, Ryall KA, Lee K, Lee S, et al. DSigDB: drug signatures database for gene setanalysis. Bioinformatics. 2015;31(18):3069-71.

13. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science.2006;313(5795):1929-35.

14. Qin C, Zhang C, Zhu F, Xu F, Chen SY, Zhang P, et al. Therapeutic target database update 2014: aresource for targeted therapeutics. Nucleic Acids Res. 2014;42(Database issue):D1118-23.

15. Davis AP, Murphy CG, Johnson R, Lay JM, Lennon-Hopkins K, Saraceni-Richards C, et al. TheComparative Toxicogenomics Database: update 2013. Nucleic Acids Res. 2013;41(Databaseissue):D1104-14

1�. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem Substance and Compounddatabases. Nucleic Acids Res. 2016;44(D1):D1202-13.

17. Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. NucleicAcids Res. 2011;39(Database issue):D52-7.

1�. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID Bioinformatics Resources:expanded annotation database and novel algorithms to better extract biology from large gene lists.Nucleic Acids Res. 2007;35(Web Server issue):W169-75.

19. Han HW, Ohn JH, Moon J, Kim JH. Yin and Yang of disease genes and death genes betweenreciprocally scale-free biological networks. Nucleic Acids Res. 2013;41(20):9209-17.

20. Clarke DJB, Kuleshov MV, Schilder BM, Torre D, Duffy ME, Keenan AB, et al. eXpression2Kinases(X2K) Web: linking expression signatures to upstream cell signaling networks. Nucleic Acids Res.2018;46(W1):W171-W9.

21. Chen H, Zhang Z, Jiang S, Li R, Li W, Zhao C, et al. New insights on human essential genes based onintegrated analysis and the construction of the HEGIAP web-based platform. Brief Bioinform.2020;21(4):1397-410.

Page 11/18

22. Isik Z, Baldow C, Cannistraci CV, Schroeder M. Drug target prioritization by perturbed gene expressionand network information. Sci Rep. 2015;5:17417.

23. Park NI, Rogan PK, Tarnowski HE, Knoll JH. Structural and genic characterization of stable genomicregions in breast cancer: relevance to chemotherapy. Mol Oncol. 2012;6(3):347-59.

24. DeLorenzo DM, Moon TS. Selection of stable reference genes for RT-qPCR in Rhodococcus opacusPD630. Sci Rep. 2018;8(1):6019.

TablesDue to technical limitations, table 1 is only available as a download in the Supplemental Files section.

Figures

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Figure 1

Construction of the drug–gene network. (A) Venn diagram of gene groups and the number of genes ineach gene set for 382 drugs (DTGs: drug-target genes; DTSGs: drug-target and sensitive genes; DSGs:drug-sensitive genes). (B) Process of the drug–gene network construction.

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Figure 2

Degree distribution of the DTSG set. (A) Reverse-cumulative distribution of KDT within the DTSG set. (B)Reverse-cumulative distribution of KDS within the DTSG set. (C) Reciprocal relationships of themultidimensional network shown through three-dimensional plots.

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Figure 3

Reverse-cumulative distribution of KDT for D1 and the DSTG set; reverse-cumulative distribution of KDSfor D3 and the DTSG set. (A) Reverse-cumulative distribution of KDT for 720 genes from the D1 targetgenes. (B) Reverse-cumulative distribution of KDSN for 9,233 genes from the D3 sensitive genes. (C)Reverse-cumulative distribution of KDTN for 463 genes from the DTSG set. (C) Reverse-cumulativedistribution of KDSN for 463 genes from the DTSG set.

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Figure 4

The drug–gene subnetwork of the DTSG set. The subnetwork of the drug–gene multidimensionalnetwork included about 5% of the drugs in the DTSG set.

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Figure 5

Gene Ontology analysis of each gene set. Top 10 GO terms for (A) cellular component, (B) biologicalprocess, and (C) molecular function from each gene set. (D) Top 8 KEGG pathway terms from each geneset.

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Figure 6

(A) Binding frequency of transcription factors per gene involved in the DSG, DTG, and DTSG sets. (B) Thenumber of transcription factors derived from the DSG, DTG, and DTSG sets.

Figure 7

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Cellular component analysis of the core genes from each gene set.

Supplementary Files

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Table1.xlsx

Supplementary�gure1.gif